A Contactless Multi-Modal Sensing Approach for Material Assessment and Recovery in Building Deconstruction

Sophia Cabral, Mikita Klimenka, Fopefoluwa Bademosi (), Damon Lau (), Stefanie Pender, Lorenzo Villaggi, James Stoddart, James Donnelly, Peter Storey and David Benjamin
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Sophia Cabral: Graduate School of Design, Harvard University, 48 Quincy St, Cambridge, MA 02138, USA
Mikita Klimenka: School of Architecture and Planning, Massachusetts Institute of Technology, 77 Massachusetts Ave Building 10-400, Cambridge, MA 02139, USA
Fopefoluwa Bademosi: Autodesk Research, 23 Drydock Ave, Boston, MA 02210, USA
Damon Lau: Autodesk Research, 23 Drydock Ave, Boston, MA 02210, USA
Stefanie Pender: Autodesk Research, 23 Drydock Ave, Boston, MA 02210, USA
Lorenzo Villaggi: Autodesk Research, 23 Drydock Ave, Boston, MA 02210, USA
James Stoddart: Autodesk Research, 23 Drydock Ave, Boston, MA 02210, USA
James Donnelly: Autodesk Research, 23 Drydock Ave, Boston, MA 02210, USA
Peter Storey: Autodesk Research, 23 Drydock Ave, Boston, MA 02210, USA
David Benjamin: Autodesk Research, 23 Drydock Ave, Boston, MA 02210, USA

Sustainability, 2025, vol. 17, issue 2, 1-30

Abstract: As material scarcity and environmental concerns grow, material reuse and waste reduction are gaining attention based on their potential to reduce carbon emissions and promote net-zero buildings. This study develops an innovative approach that combines multi-modal sensing technologies with machine learning to enable contactless assessment of in situ building materials for reuse potential. By integrating thermal imaging, red, green, and blue (RGB) cameras, as well as depth sensors, the system analyzes material conditions and reveals hidden geometries within existing buildings. This approach enhances material understanding by analyzing existing materials, including their compositions, histories, and assemblies. A case study on drywall deconstruction demonstrates that these technologies can effectively guide the deconstruction process, potentially reducing material costs and carbon emissions significantly. The findings highlight feasible scenarios for drywall reuse and offer insights into improving existing deconstruction techniques through automated feedback and visualization of cut lines and fastener positions. This research indicates that contactless assessment and automated deconstruction methods are technically viable, economically advantageous, and environmentally beneficial. Serving as an initial step toward novel methods to view and classify existing building materials, this study lays a foundation for future research, promoting sustainable construction practices that optimize material reuse and reduce negative environmental impact.

Keywords: deconstruction; material reuse; multi-modal sensing; artificial intelligence; machine learning; drywall; circular economy; sustainable construction; automated assessment; building materials (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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